Detecting a Frame-of-Reference Change in a Smart-Device-Based Radar System

ABSTRACT

Techniques and apparatuses are described that implement a smart-device-based radar system capable of detecting a frame-of-reference change. In particular, a radar system includes a frame-of-reference machine-learned module trained to recognize whether or not the radar system&#39;s frame of reference changes. The frame-of-reference machine-learned module analyzes complex radar data generated from at least one chirp of a reflected radar signal to analyze a relative motion of at least one object over time. By analyzing the complex radar data directly using machine learning, the radar system can operate as a motion sensor without relying on non-radar-based sensors, such as gyroscopes, inertial sensors, or accelerometers. With knowledge of whether the frame-of-reference is stationary or moving, the radar system can determine whether or not a gesture is likely to occur and, in some cases, compensate for the relative motion of the radar system itself.

RELATED APPLICATIONS

This application claims priority to and is a continuation of U.S.Utility application Ser. No. 16/911,116, filed Jun. 24, 2020 which inturn claims priority to and is a continuation application ofInternational Application No. PCT/US2019/063776, filed Nov. 27, 2019,the disclosures of which are incorporated herein by reference in theirentireties.

BACKGROUND

Radars are useful devices that can detect objects. Relative to othertypes of sensors, like a camera, a radar can provide improvedperformance in many different environmental conditions, such as lowlighting and fog, or with moving or overlapping objects. Radar can alsodetect objects through one or more occlusions, such as a purse or apocket. While radar has many advantages, there are many challengesassociated with integrating radar in electronic devices.

One challenge involves operating the radar in an electronic device thatcan move, such as a mobile device or a wearable device. With thepotential for the radar to operate while the electronic device isstationary or moving, there is an uncertainty regarding whether theradar's frame of reference is fixed or changing. This can make itchallenging for the radar to differentiate between situations in whichthe radar is stationary and an object is moving, the object isstationary and the radar is moving, or both the radar and the object aremoving.

SUMMARY

Techniques and apparatuses are described that implement asmart-device-based radar system capable of detecting aframe-of-reference change. A radar system includes a frame-of-referencemachine-learned module, which is trained to operate as a motion sensor.The frame-of-reference machine-learned module uses machine learning torecognize whether or not the radar system's frame of reference changes.In particular, the frame-of-reference machine-learned module analyzescomplex radar data generated from at least one chirp of a reflectedradar signal to identify subtle patterns in a relative motion of atleast one object over time. In some cases, the frame-of-referencemachine-learned module compares (e.g., correlates) relative motions oftwo or more objects. By analyzing the complex radar data directly usingmachine learning, the frame-of-reference machine-learned module candetermine whether the radar system's frame of reference changes withoutrelying on non-radar-based sensors, such as gyroscopes, inertialsensors, or accelerometers. With knowledge of whether theframe-of-reference is stationary or moving, the radar system candetermine whether or not a gesture is likely to occur and, in somecases, compensate for the relative motion of the radar system itself

Aspects described below include a method performed by a radar system fordetecting a frame-of-reference change. The method includes transmittinga first radar transmit signal and receiving a first radar receivesignal. The first radar receive signal comprises a version of the firstradar transmit signal that is reflected by at least one object. Themethod also includes generating complex radar data based on the firstradar receive signal. The method additionally includes analyzing thecomplex radar data using machine learning to detect a change in theradar system's frame of reference.

Aspects described below also include an apparatus comprising a radarsystem configured to perform any of the described methods.

Aspects described below include a computer-readable storage mediacomprising computer-executable instructions that, responsive toexecution by a processor, implement a frame-of-reference machine-learnedmodule configured to accept complex radar data associated with a radarreceive signal that is reflected by at least one object. Theframe-of-reference machine-learned module is also configured to analyzethe complex radar data using machine learning to generateframe-of-reference data. The frame-of-reference machine-learned moduleis additionally configured to determine, based on the frame-of-referencedata, whether or not an antenna array that received the radar receivesignal is stationary or moving.

Aspects described below also include a system with machine-learningmeans for detecting a frame-of-reference change based on complex radardata.

BRIEF DESCRIPTION OF THE DRAWINGS

Apparatuses for and techniques implementing a smart-device-based radarsystem capable of detecting a frame-of-reference change are describedwith reference to the following drawings. The same numbers are usedthroughout the drawings to reference like features and components:

FIG. 1 illustrates example environments in which a smart-device-basedradar system capable of detecting a frame-of-reference change can beimplemented.

FIG. 2-1 illustrates an example implementation of a radar system as partof a smart device.

FIG. 2-2 illustrates an example implementation of a frame-of-referencemachine-learned module.

FIG. 3-1 illustrates operation of an example radar system.

FIG. 3-2 illustrates an example radar framing structure for detecting aframe-of-reference change.

FIG. 4 illustrates an example antenna array and an example transceiverof a radar system.

FIG. 5 illustrates an example scheme implemented by a radar system fordetecting a frame-of-reference change.

FIG. 6 illustrates an example portion of a hardware-abstraction modulefor detecting a frame-of-reference change.

FIG. 7-1 illustrates an example space time neural network for detectinga frame-of-reference change.

FIG. 7-2 illustrates an example chirp-level analysis module of a spacetime neural network.

FIG. 7-3 illustrates an example feature-level analysis module of a spacetime neural network.

FIG. 7-4 illustrates an example main-level analysis module of a spacetime neural network.

FIG. 8 illustrates an example method for performing operations of asmart-device-based radar system capable of detecting aframe-of-reference change.

FIG. 9 illustrates an example computing system embodying, or in whichtechniques may be implemented that enable use of, a radar system capableof detecting a frame-of-reference change.

DETAILED DESCRIPTION Overview

Integrating a radar system within an electronic device can bechallenging. One challenge involves operating the radar system in anelectronic device that can move, such as a mobile device or a wearabledevice. Due to the potential for the radar system to operate while theelectronic device is stationary or moving, there is an uncertaintyregarding whether the radar system's frame of reference is fixed orchanging. This can make it challenging for the radar system todifferentiate between situations in which the radar system is stationaryand an object is moving towards the radar system, the object isstationary and the radar system is moving towards the object, or boththe radar system and the object are moving towards each other. Withoutknowing whether the frame of reference is fixed or changing, the radarsystem may attempt to recognize a gesture in a situation in which theuser is simply moving the electronic device and not performing agesture.

Some techniques may augment the radar system with additional motionsensors. However, these motion sensors can increase power consumptionand cost of the electronic device. Additionally, it can be challengingto integrated the motion sensors within a space-constrained electronicdevice. Other closed-form signal processing techniques may modeldifferent situations in which the radar system and other objects aremoving. However, it can be challenging to model non-uniform motions,including intentional and unintentional motions performed by a user.These motions can include situations in which the user handles anelectronic device that includes the radar system or situations in whichthe user moves while in proximity to the radar system.

In contrast, this document describes techniques and devices thatimplement a smart-device-based radar system capable of detecting aframe-of-reference change. A radar system includes a frame-of-referencemachine-learned module, which is trained to operate as a motion sensor.The frame-of-reference machine-learned module uses machine learning torecognize whether or not the radar system's frame of reference changes.In particular, the frame-of-reference machine-learned module analyzescomplex radar data generated from at least one chirp of a reflectedradar signal to identify subtle patterns in a relative motion of atleast one object over time. In some cases, the frame-of-referencemachine-learned module compares (e.g., correlates) relative motions oftwo or more objects. By analyzing the complex radar data directly usingmachine learning, the frame-of-reference machine-learned module candetermine whether the radar system's frame of reference changes withoutrelying on non-radar-based sensors, such as gyroscopes, inertialsensors, or accelerometers. With knowledge of whether theframe-of-reference is stationary or moving, the radar system candetermine whether or not a gesture is likely to occur and, in somecases, compensate for the relative motion of the radar system itself.

Example Environment

FIG. 1 is an illustration of example environments 100-1 to 100-8 inwhich techniques using, and an apparatus including, a smart-device-basedradar system capable of detecting a frame-of-reference may be embodied.In the depicted environments 100-1 to 100-8, a smart device 104 includesa radar system 102 capable of detecting one or more objects (e.g.,users) using machine learning. The smart device 104 is shown to be asmartphone in environments 100-1 to 100-7 and a smart vehicle in theenvironment 100-8.

In the environments 100-1 to 100-4 and 100-6, a user performs differenttypes of gestures, which are detected by the radar system 102. In somecases, the user performs a gesture using an appendage or body part.Alternatively, the user can also perform a gesture using a stylus, ahand-held object, a ring, or any type of material that can reflect radarsignals.

In environment 100-1, the user makes a scrolling gesture by moving ahand above the smart device 104 along a horizontal dimension (e.g., froma left side of the smart device 104 to a right side of the smart device104). In the environment 100-2, the user makes a reaching gesture, whichdecreases a distance between the smart device 104 and the user's hand.The users in environment 100-3 make hand gestures to play a game on thesmart device 104. In one instance, a user makes a pushing gesture bymoving a hand above the smart device 104 along a vertical dimension(e.g., from a bottom side of the smart device 104 to a top side of thesmart device 104). In the environment 100-4, the smart device 104 isstored within a purse, and the radar system 102 providesoccluded-gesture recognition by detecting gestures that are occluded bythe purse. In the environment 100-6, the user waves their hand in frontof the smart device 104.

The radar system 102 can also recognize other types of gestures ormotions not shown in FIG. 1 . Example types of gestures include aknob-turning gesture in which a user curls their fingers to grip animaginary doorknob and rotate their fingers and hand in a clockwise orcounter-clockwise fashion to mimic an action of turning the imaginarydoorknob. Another example type of gesture includes a spindle-twistinggesture, which a user performs by rubbing a thumb and at least one otherfinger together. The gestures can be two-dimensional, such as those usedwith touch-sensitive displays (e.g., a two-finger pinch, a two-fingerspread, or a tap). The gestures can also be three-dimensional, such asmany sign-language gestures, e.g., those of American Sign Language (ASL)and other sign languages worldwide. Upon detecting each of thesegestures, the smart device 104 can perform an action, such as displaynew content, move a cursor, activate one or more sensors, open anapplication, and so forth. In this way, the radar system 102 providestouch-free control of the smart device 104.

In the environment 100-7, the radar system 102 generates athree-dimensional map of a surrounding environment for contextualawareness. The radar system 102 also detects and tracks multiple usersto enable both users to interact with the smart device 104. The radarsystem 102 can also perform vital-sign detection. In the environment100-8, the radar system 102 monitors vital signs of a user that drives avehicle. Example vital signs include a heart rate and a respirationrate. If the radar system 102 determines that the driver is fallingasleep, for instance, the radar system 102 can cause the smart device104 to alert the user. Alternatively, if the radar system 102 detects alife-threatening emergency, such as a heart attack, the radar system 102can cause the smart device 104 to alert a medical professional oremergency services.

In different environments 100-1 to 100-8, the radar system 102 may bestationary or moving. In the environments 100-1 to 100-3 and 100-7, forinstance, the smart device 104 is positioned on a non-moving surface,such as a table. In this situation, the radar system 102′s frame ofreference is stationary (e.g., fixed) 106 while one or more objects(e.g., users) move. In contrast, environment 100-5 illustrates asituation in which a portion of the user that is visible to the radarsystem 102 remains stationary and the radar system 102 moves. Inparticular, the user swings their arm in a manner that causes the smartdevice 104 to pass by their leg. In this case, the radar system 102′sframe of reference is moving 108 (e.g., changing) while the portion ofthe user observed by the radar system 102 is stationary. In still otherenvironments, both the user and the radar system 102 move. As anexample, the users in environments 100-4 and 100-6 move the radar system102 (intentionally or unintentionally) while performing a gesture. Todistinguish between these different situations, the radar system 102uses machine learning to detect a frame-of-reference change, as furtherdescribed with respect to FIG. 2 .

Some implementations of the radar system 102 are particularlyadvantageous as applied in the context of smart devices 104, for whichthere is a convergence of issues. This can include a need forlimitations in a spacing and layout of the radar system 102 and lowpower. Exemplary overall lateral dimensions of the smart device 104 canbe, for example, approximately eight centimeters by approximatelyfifteen centimeters. Exemplary footprints of the radar system 102 can beeven more limited, such as approximately four millimeters by sixmillimeters with antennas included. Exemplary power consumption of theradar system 102 may be on the order of a few milliwatts to tens ofmilliwatts (e.g., between approximately two milliwatts and twentymilliwatts). The requirement of such a limited footprint and powerconsumption for the radar system 102 enables the smart device 104 toinclude other desirable features in a space-limited package (e.g., acamera sensor, a fingerprint sensor, a display, and so forth). The smartdevice 104 and the radar system 102 are further described with respectto FIG. 2 .

FIG. 2-1 illustrates the radar system 102 as part of the smart device104. The smart device 104 is illustrated with various non-limitingexample devices including a desktop computer 104-1, a tablet 104-2, alaptop 104-3, a television 104-4, a computing watch 104-5, computingglasses 104-6, a gaming system 104-7, a microwave 104-8, and a vehicle104-9. Other devices may also be used, such as a home service device, asmart speaker, a smart thermostat, a security camera, a baby monitor, aWi-Fi™ router, a drone, a trackpad, a drawing pad, a netbook, ane-reader, a home-automation and control system, a wall display, andanother home appliance. Note that the smart device 104 can be wearable,non-wearable but mobile, or relatively immobile (e.g., desktops andappliances). The radar system 102 can be used as a stand-alone radarsystem or used with, or embedded within, many different smart devices104 or peripherals, such as in control panels that control homeappliances and systems, in automobiles to control internal functions(e.g., volume, cruise control, or even driving of the car), or as anattachment to a laptop computer to control computing applications on thelaptop.

The smart device 104 includes one or more computer processors 202 andcomputer-readable media 204, which includes memory media and storagemedia. Applications and/or an operating system (not shown) embodied ascomputer-readable instructions on the computer-readable media 204 can beexecuted by the computer processor 202 to provide some of thefunctionalities described herein. The computer-readable media 204 alsoincludes a radar-based application 206, which uses radar data generatedby the radar system 102 to perform a function, such as motion sensing,presence detection, gesture-based touch-free control, collisionavoidance for autonomous driving, human vital-sign notification, and soforth.

The smart device 104 can also include a network interface 208 forcommunicating data over wired, wireless, or optical networks. Forexample, the network interface 208 may communicate data over alocal-area-network (LAN), a wireless local-area-network (WLAN), apersonal-area-network (PAN), a wire-area-network (WAN), an intranet, theInternet, a peer-to-peer network, point-to-point network, a meshnetwork, and the like. The smart device 104 may also include a display(not shown).

The radar system 102 includes a communication interface 210 to transmitthe radar data to a remote device, though this need not be used when theradar system 102 is integrated within the smart device 104. In general,the radar data provided by the communication interface 210 is in aformat usable by the radar-based application 206.

The radar system 102 also includes at least one antenna array 212 and atleast one transceiver 214 to transmit and receive radar signals. Theantenna array 212 includes at least one transmit antenna element and atleast two receive antenna elements. In some situations, the antennaarray 212 includes multiple transmit antenna elements to implement amultiple-input multiple-output (MIMO) radar capable of transmittingmultiple distinct waveforms at a given time (e.g., a different waveformper transmit antenna element). The antenna elements can be circularlypolarized, horizontally polarized, vertically polarized, or acombination thereof.

The receive antenna elements of the antenna array 212 can be positionedin a one-dimensional shape (e.g., a line) or a two-dimensional shape(e.g., a rectangular arrangement, a triangular arrangement, or an “L”shape arrangement) for implementations that include three or morereceive antenna elements. The one-dimensional shape enables the radarsystem 102 to measure one angular dimension (e.g., an azimuth or anelevation) while the two-dimensional shape enables the radar system 102to measure two angular dimensions (e.g., to determine both an azimuthangle and an elevation angle of the object 302). An element spacingassociated with the receive antenna elements can be less than, greaterthan, or equal to half a center wavelength of the radar signal.

Using the antenna array 212, the radar system 102 can form beams thatare steered or un-steered, wide or narrow, or shaped (e.g., hemisphere,cube, fan, cone, cylinder). The steering and shaping can be achievedthrough analog beamforming or digital beamforming. The one or moretransmitting antenna elements can have, for instance, an un-steeredomnidirectional radiation pattern or can produce a wide steerable beamto illuminate a large volume of space. To achieve target angularaccuracies and angular resolutions, the receiving antenna elements canbe used to generate hundreds or thousands of narrow steered beams withdigital beamforming. In this way, the radar system 102 can efficientlymonitor an external environment and detect one or more users.

The transceiver 214 includes circuitry and logic for transmitting andreceiving radar signals via the antenna array 212. Components of thetransceiver 214 can include amplifiers, mixers, switches,analog-to-digital converters, or filters for conditioning the radarsignals. The transceiver 214 also includes logic to performin-phase/quadrature (I/Q) operations, such as modulation ordemodulation. A variety of modulations can be used, including linearfrequency modulations, triangular frequency modulations, steppedfrequency modulations, or phase modulations. Alternatively, thetransceiver 214 can produce radar signals having a relatively constantfrequency or a single tone. The transceiver 214 can be configured tosupport continuous-wave or pulsed radar operations.

A frequency spectrum (e.g., range of frequencies) that the transceiver214 uses to generate the radar signals can encompass frequencies between1 and 400 gigahertz (GHz), between 4 and 100 GHz, between 1 and 24 GHz,between 2 and 4 GHz, between 57 and 64 GHz, or at approximately 2.4 GHz.In some cases, the frequency spectrum can be divided into multiplesub-spectrums that have similar or different bandwidths. The bandwidthscan be on the order of 500 megahertz (MHz), 1 GHz, 2 GHz, and so forth.Different frequency sub-spectrums may include, for example, frequenciesbetween approximately 57 and 59 GHz, 59 and 61 GHz, or 61 and 63 GHz.Although the example frequency sub-spectrums described above arecontiguous, other frequency sub-spectrums may not be contiguous. Toachieve coherence, multiple frequency sub-spectrums (contiguous or not)that have a same bandwidth may be used by the transceiver 214 togenerate multiple radar signals, which are transmitted simultaneously orseparated in time. In some situations, multiple contiguous frequencysub-spectrums may be used to transmit a single radar signal, therebyenabling the radar signal to have a wide bandwidth.

The radar system 102 also includes one or more system processors 216 anda system media 218 (e.g., one or more computer-readable storage media).The system media 218 optionally includes a hardware-abstraction module220 and at least one circular buffer 224. The system media 218 alsoincludes at least one frame-of-reference machine-learned (ML) module222. The hardware-abstraction module 220, the frame-of-referencemachine-learned module 222, and the circular buffer 224 can beimplemented using hardware, software, firmware, or a combinationthereof. In this example, the system processor 216 implements thehardware-abstraction module 220 and the frame-of-referencemachine-learned module 222. The system processor 216 or a memorycontroller can implement and manage the circular buffer 224. Together,the hardware-abstraction module 220, the frame-of-referencemachine-learned module 222, and the circular buffer 224 enable thesystem processor 216 to process responses from the receive antennaelements in the antenna array 212 to detect a user, determine a positionof the object, recognize a gesture performed by the user, and/or detecta frame-of-reference change.

In an alternative implementation (not shown), the hardware-abstractionmodule 220, the frame-of-reference machine-learned module 222, or thecircular buffer 224 are included within the computer-readable media 204and implemented by the computer processor 202. This enables the radarsystem 102 to provide the smart device 104 raw data via thecommunication interface 210 such that the computer processor 202 canprocess the raw data for the radar-based application 206.

The hardware-abstraction module 220 transforms raw data provided by thetransceiver 214 into hardware-agnostic complex radar data, which can beprocessed by the frame-of-reference machine-learned module 222. Inparticular, the hardware-abstraction module 220 conforms complex radardata from a variety of different types of radar signals to an expectedinput of the frame-of-reference machine-learned module 222. This enablesthe frame-of-reference machine-learned module 222 to process differenttypes of radar signals received by the radar system 102, including thosethat utilize different modulations schemes for frequency-modulatedcontinuous-wave radar, phase-modulated spread spectrum radar, or impulseradar. The hardware-abstraction module 220 can also normalize complexradar data from radar signals with different center frequencies,bandwidths, transmit power levels, or pulsewidths.

Additionally, the hardware-abstraction module 220 conforms complex radardata generated using different hardware architectures. Differenthardware architectures can include different antenna arrays 212positioned on different surfaces of the smart device 104 or differentsets of antenna elements within an antenna array 212. By using thehardware-abstraction module 220, the frame-of-reference machine-learnedmodule 222 can process complex radar data generated by different sets ofantenna elements with different gains, different sets of antennaelements of various quantities, or different sets of antenna elementswith different antenna element spacings.

With the hardware-abstraction module 220, the frame-of-referencemachine-learned module 222 can operate in radar systems 102 withdifferent limitations that affect the available radar modulationschemes, transmission parameters, or types of hardware architectures.The hardware-abstraction module 220 is further described with respect toFIGS. 5 and 6 .

The frame-of-reference machine-learned module 222 uses machine learningto analyze complex radar data, such as the hardware-agnostic complexradar data, and determine whether the radar system 102′s frame ofreference is stationary or moving. In particular, the frame-of-referencemachine-learned module 222 analyzes a relative motion of at least oneobject over time and/or compares (e.g., correlates) relative motions oftwo or more objects. The frame-of-reference machine-learned module 222can analyze both magnitude and phase information of the complex radardata to improve accuracies for detecting a change in theframe-of-reference. A design of the frame-of-reference machine-learnedmodule 222 can be tailored to support smart devices 104 with differentamounts of available memory, different amounts available power, ordifferent computational capabilities. In some cases, theframe-of-reference machine-learned module 222 includes a suite ofmachine-learning architectures that can be individually selectedaccording to the type of smart device 104 or the radar-based application206.

In some cases, the frame-of-reference machine-learned module 222implements a portion of a gesture recognition module (not shown) orprovides frame-of-reference data to the gesture recognition module. Thegesture recognition module can use the frame-of-reference data todetermine whether or not a gesture is likely performed. For example, ifthe frame-of-reference data indicates that the radar system 102's frameof reference is moving, the gesture recognition module can determinethat the detected motion of the user is due, at least in part, to themoving frame of reference. As such, it is less likely that the user isperforming an intentional gesture while moving the radar system 102. Inthis case, the gesture recognition module may decide to not performgesture recognition in order to avoid potential false alarms. Incontrast, if the frame-of-reference data indicates that the radar system102's frame of reference is stationary, the gesture recognition modulecan determine that the detected motion of the user likely represents anintentional gesture. In this case, the gesture recognition module maydecide to perform gesture recognition.

Additionally or alternatively, the frame-of-reference machine-learnedmodule 222 determines one or more characteristics regarding the changein the frame of reference, which describes a trajectory of the radarsystem 102. These characteristics can be used to update a clutter mapthat is maintained by the radar system 102 or determine an absolutemotion of the object by removing motion components caused by the motionof the radar system 102, for instance. In some cases, the radar system102 maps an external environment and determines a position of the smartdevice 104 relative to stationary objects within the externalenvironment, such as furniture, walls, buildings, or street signs. Usingthe frame-of-reference machine-learned module 222, the radar system 102can update its known position within the external environment.

In some implementations, the radar system 102 obviates the use of othermotion sensors, and can be used to determine an orientation,acceleration, or position of the smart device 104. In this manner,space-constrained devices, such as wearable devices, can utilize theradar system 102 to provide motion data in addition to data for theradar-based application 206.

The circular buffer 224 is a fixed-size memory buffer implemented usingan allocated portion of memory within the system media 218. The circularbuffer 224 includes multiple memory elements and provides afirst-in-first-out queue in which data is sequentially stored andaccessed using the memory elements. Once all of the memory elementsstore data, the oldest data stored is overwritten. In someimplementations of the frame-of-reference machine-learned module 222,the circular buffer 224 is implemented between two stages of theframe-of-reference machine-learned module 222. As such, the circularbuffer 224 stores data generated by a first stage of theframe-of-reference machine-learned module 222 and enables a second stageof the frame-of-reference machine-learned module 222 to access thestored data. The circular buffer 224 is further described with respectto FIG. 7-3 . With or without the hardware-abstraction module 220 andthe circular buffer 224, the frame-of-reference machine-learned module222 can implement, at least partially, detection of aframe-of-reference, as further described with respect to FIG. 2-2 .

FIG. 2-2 illustrates an example implementation of the frame-of-referencemachine-learned module 222. The frame-of-reference machine-learnedmodule 222 can include one or more artificial neural networks (referredto herein as neural networks). A neural network includes a group ofconnected nodes (e.g., neurons or perceptrons), which are organized intoone or more layers. As an example, the frame-of-referencemachine-learned module 222 includes a deep neural network 230, whichincludes an input layer 232, an output layer 234, and one or more hiddenlayers 236 positioned between the input layer 232 and the output layer234. The nodes of the deep neural network 230 can be partially-connectedor fully connected between the layers.

The input layer 232 includes multiple inputs 238-1, 2388-2 . . . 238-X,where X represents a positive integer. The multiple hidden layers 236include layers 240-1, 240-2 . . . 240-W, where W represents a positiveinteger. Each hidden layer 236 includes multiple neurons, such asneurons 242-1, 242-2 . . . 242-Y, where Y is a positive integer. Eachneuron 242 is connected to at least one other neuron 242 in anotherhidden layer 236. A quantity of neurons 242 can be similar or differentfor different hidden layers 236. In some cases, a hidden layer 236 canbe a replica of a previous layer (e.g., layer 240-2 can be a replica oflayer 240-1). The output layer 234 includes outputs 244-1, 244-2 . . .244-Z, where Z represents a positive integer. A variety of differentdeep neural networks 230 can be used with various quantities of inputs238, hidden layers 236, neurons 242, and outputs 244.

In some cases, the deep neural network 230 is a recurrent deep neuralnetwork (e.g., a long short-term memory (LSTM) recurrent deep neuralnetwork) with connections between nodes forming a cycle to retaininformation from a previous portion of an input data sequence for asubsequent portion of the input data sequence. In other cases, the deepneural network 230 is a feed-forward deep neural network in which theconnections between the nodes do not form a cycle. Additionally oralternatively, the frame-of-reference machine-learned module 222 caninclude another type of neural network, such as a convolutional neuralnetwork.

Generally, a machine-learning architecture of the frame-of-referencemachine-learned module 222 can be tailored based on available power,available memory, or computational capability. The machine-learningarchitecture can also be tailored to implement a classification modelthat indicates whether or not a frame-of-reference change is detected, aregression model that indicates probabilities associated with theframe-of-reference being stationary and moving, or another regressionmodel that characterizes the changes to the frame of reference (e.g.,describes the trajectory of the radar system 102 in terms of distance,direction, and/or velocity).

During operation, complex radar data 246 is provided to the input layer232. The complex radar data 246 can include a complex range-Doppler map,complex interferometry data, multiple digital beat signals associatedwith a reflected radar signal, or frequency-domain representations ofthe multiple digital beat signals, as further described with respect toFIG. 5 . Generally, the complex radar data 246 includes a matrix (orvector) of complex numbers. In some cases, each element of the matrix isprovided to one of the inputs 238-1 to 238-X. In other cases, a quantityof contiguous elements are combined and provided to one of the inputs238-1 to 238-X.

Each neuron 242 in the hidden layers 236 analyzes a different section orportion of the complex radar data 246 using an activation function. Theneuron 242 activates (or inversely activates) when a specific type ofcharacteristic is detected within the complex radar data 246. An exampleactivation function can include, for example, a non-linear function suchas a hyperbolic tangent function. Towards the top of FIG. 2-2 , a neuron242 is shown to obtain inputs IN₁W₁, IN₁W₂ . . . IN_(x)W_(x) and a biasW₀, where IN₁, IN₂ . . . IN_(x) correspond to outputs of a previousinput or hidden layer (e.g., the layer 240-1 in FIG. 2-2 ) and W₁, W₂ .. . W_(x) correspond to respective weights that are applied to IN₁, IN₂. . . IN_(x). An output (OUT) that is generated by the neuron 242 isdetermined based on the activation function f(z). In the depictedexample, X is equal to Y for a fully-connected network. The output OUTcan be scaled by another weight and provided as an input to anotherlayer 240 or the output layer 234.

At the output layer 234, the frame-of-reference machine-learned module222 generates frame-of-reference data 248. As described above, theframe-of-reference data 248 can include a Boolean value that indicateswhether or not a frame-of-reference change is detected, continuousvalues that indicate a first probability that the frame-of-reference isstationary and a second probability that the frame-of-reference ismoving, or other continuous values that indicate the distance,direction, and/or speed associated with a change to theframe-of-reference.

With training, the frame-of-reference machine-learned module 222 candetect a frame-of-reference change by recognizing subtle patterns in arelative motion of at least one object. Additionally or alternatively,the frame-of-reference machine-learned module 222 also detects theframe-of-reference change by comparing (e.g., correlating) relativemotions of two or more objects. The frame-of-reference machine-learnedmodule 222 can compare different characteristics of the relativemotions, including changes in position (e.g., range, azimuth, orelevation), range rate or Doppler, or velocity.

In some implementations, the frame-of-reference machine-learned module222 relies on supervised learning and can use measured (e.g., real) datafor machine-learning training purposes. In general, the real data iscollected during situations in which the radar system 102 is moving andthe object (e.g., the user) is stationary, the object (e.g., the user)is moving and the radar system 102 is stationary, and both the radarsystem 102 and the object are moving. Other scenarios can also includeother objects that are moving or not moving. Some of the motions aremade by a human, which can be difficult for closed-form signalprocessing techniques to identify. Training enables theframe-of-reference machine-learned module 222 to learn a non-linearmapping function for detecting a state of the frame-of-reference basedon the complex radar data 246. In other implementations, theframe-of-reference machine-learned module 222 relies on unsupervisedlearning to determine the non-linear mapping function.

An example offline training procedure uses a motion-capture system togenerate truth data for training the frame-of-reference machine-learnedmodule 222. The motion-capture system can include multiple opticalsensors, such as infrared-sensors or cameras, and measures positions ofmultiple markers that are placed on different portions of a person'sbody, such as on an arm, a hand, a torso, or a head. Some markers arealso positioned on the smart device 104 or the radar system 102. Whilethe person or the radar system 102 moves, the complex radar data 246from the radar system 102 and position data from the motion-capturesystem are recorded. The complex radar data 246 represents trainingdata. The position data recorded from the motion-capture system isconverted into a format that conforms with the frame-of-reference data248 and represents truth data. The truth data and the training data aresynchronized in time, and provided to the frame-of-referencemachine-learned module 222. The frame-of-reference machine-learnedmodule 222 generates estimates of the frame-of-reference data 248 basedon the training data, and determines amounts of error between theestimated frame-of-reference data 248 and the truth data. Theframe-of-reference machine-learned module 222 adjusts machine-learningparameters (e.g., weights and biases) to minimize these errors. Based onthis offline training procedure, the determined weights and biases arepre-programmed into the frame-of-reference machine-learned module 222 toenable detection of subsequent frame-of-reference changes using machinelearning. In some cases, the offline training procedure can provide arelatively noise-free environment and high-resolution truth data fortraining the frame-of-reference machine-learned module 222.

Additionally or alternatively, a real-time training procedure can useavailable sensors within the smart device 104 to generate truth data fortraining the frame-of-reference machine-learned module 222. In thiscase, a training procedure can be initiated by a user of the smartdevice 104. While the user moves around the smart device 104 and/ormoves the smart device 104, data from one or more sensor (e.g., anaccelerometer, a gyroscope, an inertial sensor, a camera, or aninfra-red sensor) of the smart device 104 and the complex radar data 246generated by the radar system 102 are collected and provided to theframe-of-reference machine-learned module 222. The frame-of-referencemachine-learned module 222 determines or adjusts machine-learningparameters to minimize errors between the estimated frame-of-referencedata 248 and the truth data provided by the sensor. Using the real-timetraining procedure, the frame-of-reference machine-learned module 222can be tailored to the user, account for current environmentalconditions, and account for a current position or orientation of thesmart device 104.

FIG. 3-1 illustrates an example operation of the radar system 102. Inthe depicted configuration, the radar system 102 is implemented as afrequency-modulated continuous-wave radar. However, other types of radararchitectures can be implemented, as described above with respect toFIG. 2 . In environment 300, a user 302 is located at a particular slantrange 304 from the radar system 102. To detect the user 302, the radarsystem 102 transmits a radar transmit signal 306. At least a portion ofthe radar transmit signal 306 is reflected by the user 302. Thisreflected portion represents a radar receive signal 308. The radarsystem 102 receives the radar receive signal 308 and processes the radarreceive signal 308 to extract data for the radar-based application 206.As depicted, an amplitude of the radar receive signal 308 is smallerthan an amplitude of the radar transmit signal 306 due to lossesincurred during propagation and reflection.

The radar transmit signal 306 includes a sequence of chirps 310-1 to310-N, where N represents a positive integer greater than one. The radarsystem 102 can transmit the chirps 310-1 to 310-N in a continuous burstor transmit the chirps 310-1 to 310-N as time-separated pulses, asfurther described with respect to FIG. 3-2 . A duration of each chirp310-1 to 310-N can be on the order of tens or thousands of microseconds(e.g., between approximately 30 microseconds (μs) and 5 milliseconds(ms)), for instance.

Individual frequencies of the chirps 310-1 to 310-N can increase ordecrease over time. In the depicted example, the radar system 102employs a two-slope cycle (e.g., triangular frequency modulation) tolinearly increase and linearly decrease the frequencies of the chirps310-1 to 310-N over time. The two-slope cycle enables the radar system102 to measure the Doppler frequency shift caused by motion of the user302. In general, transmission characteristics of the chirps 310-1 to310-N (e.g., bandwidth, center frequency, duration, and transmit power)can be tailored to achieve a particular detection range, rangeresolution, or doppler sensitivity for detecting one or morecharacteristics the user 302 or one or more actions performed by theuser 302.

At the radar system 102, the radar receive signal 308 represents adelayed version of the radar transmit signal 306. The amount of delay isproportional to the slant range 304 (e.g., distance) from the antennaarray 212 of the radar system 102 to the user 302. In particular, thisdelay represents a summation of a time it takes for the radar transmitsignal 306 to propagate from the radar system 102 to the user 302 and atime it takes for the radar receive signal 308 to propagate from theuser 302 to the radar system 102. If the user 302 and/or the radarsystem 102 is moving, the radar receive signal 308 is shifted infrequency relative to the radar transmit signal 306 due to the Dopplereffect. In other words, characteristics of the radar receive signal 308are dependent upon motion of the hand and/or motion of the radar system102. Similar to the radar transmit signal 306, the radar receive signal308 is composed of one or more of the chirps 310-1 to 310-N.

The multiple chirps 310-1 to 310-N enable the radar system 102 to makemultiple observations of the user 302 over a predetermined time period.A radar framing structure determines a timing of the chirps 310-1 to310-N, as further described with respect to FIG. 3-2 .

FIG. 3-2 illustrates an example radar framing structure 312 fordetecting a frame-of-reference change. In the depicted configuration,the radar framing structure 312 includes three different types offrames. At a top level, the radar framing structure 312 includes asequence of main frames 314, which can be in the active state or theinactive state. Generally speaking, the active state consumes a largeramount of power relative to the inactive state. At an intermediatelevel, the radar framing structure 312 includes a sequence of featureframes 316, which can similarly be in the active state or the inactivestate. Different types of feature frames 316 include a pulse-modefeature frame 318 (shown at the bottom-left of FIG. 3-2 ) and aburst-mode feature frame 320 (shown at the bottom-right of FIG. 3-2 ).At a low level, the radar framing structure 312 includes a sequence ofradar frames (RF) 322, which can also be in the active state or theinactive state.

The radar system 102 transmits and receives a radar signal during anactive radar frame 322. In some situations, the radar frames 322 areindividually analyzed for basic radar operations, such as search andtrack, clutter map generation, user location determination, and soforth. Radar data collected during each active radar frame 322 can besaved to a buffer after completion of the radar frame 322 or provideddirectly to the system processor 216 of FIG. 2 .

The radar system 102 analyzes the radar data across multiple radarframes 322 (e.g., across a group of radar frames 322 associated with anactive feature frame 316) to identify a particular feature. Exampletypes of features include a particular type of motion, a motionassociated with a particular appendage (e.g., a hand or individualfingers), and a feature associated with different portions of thegesture. To detect a change in the radar system 102′s frame of referenceor recognize a gesture performed by the user 302 during an active mainframe 314, the radar system 102 analyzes the radar data associated withone or more active feature frames 316.

A duration of the main frame 314 may be on the order of milliseconds orseconds (e.g., between approximately 10 ms and 10 seconds (s)). Afteractive main frames 314-1 and 314-2 occur, the radar system 102 isinactive, as shown by inactive main frames 314-3 and 314-4. A durationof the inactive main frames 314-3 and 314-4 is characterized by a deepsleep time 324, which may be on the order of tens of milliseconds ormore (e.g., greater than 50 ms). In an example implementation, the radarsystem 102 turns off all of the active components (e.g., an amplifier,an active filter, a voltage-controlled oscillator (VCO), avoltage-controlled buffer, a multiplexer, an analog-to-digitalconverter, a phase-lock loop (PLL) or a crystal oscillator) within thetransceiver 214 to conserve power during the deep sleep time 324.

In the depicted radar framing structure 312, each main frame 314includes K feature frames 316, where K is a positive integer. If themain frame 314 is in the inactive state, all of the feature frames 316associated with that main frame 314 are also in the inactive state. Incontrast, an active main frame 314 includes J active feature frames 316and K-J inactive feature frames 316, where J is a positive integer thatis less than or equal to K. A quantity of feature frames 316 can beadjusted based on a complexity of the environment or a complexity of agesture. For example, a main frame 314 can include a few to a hundredfeature frames 316 (e.g., K may equal 2, 10, 30, 60, or 100). A durationof each feature frame 316 may be on the order of milliseconds (e.g.,between approximately 1 ms and 50 ms).

To conserve power, the active feature frames 316-1 to 316-J occur priorto the inactive feature frames 316-(J+1) to 316-K. A duration of theinactive feature frames 316-(J+1) to 316-K is characterized by a sleeptime 326. In this way, the inactive feature frames 316-(J+1) to 316-Kare consecutively executed such that the radar system 102 can be in apowered-down state for a longer duration relative to other techniquesthat may interleave the inactive feature frames 316-(J+1) to 316-K withthe active feature frames 316-1 to 316-J. Generally speaking, increasinga duration of the sleep time 326 enables the radar system 102 to turnoff components within the transceiver 214 that require longer start-uptimes.

Each feature frame 316 includes L radar frames 322, where L is apositive integer that may or may not be equal to J or K. In someimplementations, a quantity of radar frames 322 may vary acrossdifferent feature frames 316 and may comprise a few frames or hundredsof frames (e.g., L may equal 5, 15, 30, 100, or 500). A duration of aradar frame 322 may be on the order of tens or thousands of microseconds(e.g., between approximately 30 μs and 5 ms). The radar frames 322within a particular feature frame 316 can be customized for apredetermined detection range, range resolution, or doppler sensitivity,which facilitates detection of a particular feature or gesture. Forexample, the radar frames 322 may utilize a particular type ofmodulation, bandwidth, frequency, transmit power, or timing. If thefeature frame 316 is in the inactive state, all of the radar frames 322associated with that feature frame 316 are also in the inactive state.

The pulse-mode feature frame 318 and the burst-mode feature frame 320include different sequences of radar frames 322. Generally speaking, theradar frames 322 within an active pulse-mode feature frame 318 transmitpulses that are separated in time by a predetermined amount. Thisdisperses observations over time, which can make it easier for the radarsystem 102 to detect the frame-of-reference change due to larger changesin the observed chirps 310-1 to 310-N within the pulse-mode featureframe 318 relative to the burst-mode feature frame 320. In contrast, theradar frames 322 within an active burst-mode feature frame 320 transmitpulses continuously across a portion of the burst-mode feature frame 320(e.g., the pulses are not separated by a predetermined amount of time).This enables an active-burst-mode feature frame 320 to consume lesspower than the pulse-mode feature frame 318 by turning off a largerquantity of components, including those with longer start-up times, asfurther described below.

Within each active pulse-mode feature frame 318, the sequence of radarframes 322 alternates between the active state and the inactive state.Each active radar frame 322 transmits a chirp 310 (e.g., a pulse), whichis illustrated by a triangle. A duration of the chirp 310 ischaracterized by an active time 328. During the active time 328,components within the transceiver 214 are powered-on. During ashort-idle time 330, which includes the remaining time within the activeradar frame 322 and a duration of the following inactive radar frame322, the radar system 102 conserves power by turning off one or moreactive components within the transceiver 214 that have a start-up timewithin a duration of the short-idle time 330.

An active burst-mode feature frame 320 includes P active radar frames322 and L-P inactive radar frames 322, where P is a positive integerthat is less than or equal to L. To conserve power, the active radarframes 322-1 to 322-P occur prior to the inactive radar frames 322-(P+1)to 322-L. A duration of the inactive radar frames 322-(P+1) to 322-L ischaracterized by a long-idle time 332. By grouping the inactive radarframes 322-(P+1) to 322-L together, the radar system 102 can be in apowered-down state for a longer duration relative to the short-idle time330 that occurs during the pulse-mode feature frame 318. Additionally,the radar system 102 can turn off additional components within thetransceiver 214 that have start-up times that are longer than theshort-idle time 330 and shorter than the long-idle time 332.

Each active radar frame 322 within an active burst-mode feature frame320 transmits a portion of the chirp 310. In this example, the activeradar frames 322-1 to 322-P alternate between transmitting a portion ofthe chirp 310 that increases in frequency and a portion of the chirp 310that decreases in frequency.

The radar framing structure 312 enables power to be conserved throughadjustable duty cycles within each frame type. A first duty cycle 334 isbased on a quantity of active feature frames 316 (J) relative to a totalquantity of feature frames 316 (K). A second duty cycle 336 is based ona quantity of active radar frames 322 (e.g., L/2 or P) relative to atotal quantity of radar frames 322 (L). A third duty cycle 338 is basedon a duration of the chirp 310 relative to a duration of a radar frame322.

Consider an example radar framing structure 312 for a power state thatconsumes approximately 2 milliwatts (mW) of power and has a main-frameupdate rate between approximately 1 and 4 hertz (Hz). In this example,the radar framing structure 312 includes a main frame 314 with aduration between approximately 250 ms and 1 second. The main frame 314includes thirty-one pulse-mode feature frames 318 (e.g., K is equal to31). One of the thirty-one pulse-mode feature frames 318 is in theactive state. This results in the duty cycle 334 being approximatelyequal to 3.2%. A duration of each pulse-mode feature frame 318 isbetween approximately 8 and 32 ms. Each pulse-mode feature frame 318 iscomposed of eight radar frames 322 (e.g., L is equal to 8). Within theactive pulse-mode feature frame 318, all eight radar frames 322 are inthe active state. This results in the duty cycle 336 being equal to100%. A duration of each radar frame 322 is between approximately 1 and4 ms. An active time 328 within each of the active radar frames 322 isbetween approximately 32 and 128 μs. As such, the resulting duty cycle338 is approximately 3.2%. This example radar framing structure 312 hasbeen found to yield good performance results. These good performanceresults are in terms of good frame-of-reference detection, gesturerecognition, and presence detection while also yielding good powerefficiency results in the application context of a handheld smartphonein a low-power state. Generation of the radar transmit signal 306 (ofFIG. 3-1 ) and the processing of the radar receive signal 308 (of FIG.3-1 ) are further described with respect to FIG. 4 .

FIG. 4 illustrates an example antenna array 212 and an exampletransceiver 214 of the radar system 102. In the depicted configuration,the transceiver 214 includes a transmitter 402 and a receiver 404. Thetransmitter 402 includes at least one voltage-controlled oscillator 406and at least one power amplifier 408. The receiver 404 includes at leasttwo receive channels 410-1 to 410-M, where M is a positive integergreater than one. Each receive channel 410-1 to 410-M includes at leastone low-noise amplifier 412, at least one mixer 414, at least one filter416, and at least one analog-to-digital converter 418.

The antenna array 212 includes at least one transmit antenna element 420and at least two receive antenna elements 422-1 to 422-M. The transmitantenna element 420 is coupled to the transmitter 402. The receiveantenna elements 422-1 to 422-M are respectively coupled to the receivechannels 410-1 to 410-M.

During transmission, the voltage-controlled oscillator 406 generates afrequency-modulated radar signal 424 at radio frequencies. The poweramplifier 408 amplifies the frequency-modulated radar signal 424 fortransmission via the transmit antenna element 420. The transmittedfrequency-modulated radar signal 424 is represented by the radartransmit signal 306, which can include multiple chirps 310-1 to 310-Nbased on the radar framing structure 312 of FIG. 3-2 . As an example,the radar transmit signal 306 is generated according to the burst-modefeature frame 320 of FIG. 3-2 and includes 16 chirps 310 (e.g., N equals16).

During reception, each receive antenna element 422-1 to 422-M receives aversion of the radar receive signal 308-1 to 308-M. In general, relativephase differences between these versions of the radar receive signals308-1 to 308-M are due to differences in locations of the receiveantenna elements 422-1 to 422-M. Within each receive channel 410-1 to410-M, the low-noise amplifier 412 amplifies the radar receive signal308, and the mixer 414 mixes the amplified radar receive signal 308 withthe frequency-modulated radar signal 424. In particular, the mixerperforms a beating operation, which downconverts and demodulates theradar receive signal 308 to generate a beat signal 426.

A frequency of the beat signal 426 represents a frequency differencebetween the frequency-modulated radar signal 424 and the radar receivesignal 308, which is proportional to the slant range 304 of FIG. 3-1 .Although not shown, the beat signal 426 can include multiplefrequencies, which represents reflections from different portions of theuser 302 (e.g., different fingers, different portions of a hand, ordifferent body parts). In some cases, these different portions move atdifferent speeds, move in different directions, or are positioned atdifferent slant ranges relative to the radar system 102.

The filter 416 filters the beat signal 426, and the analog-to-digitalconverter 418 digitizes the filtered beat signal 426. The receivechannels 410-1 to 410-M respectively generate digital beat signals 428-1to 428-M, which are provided to the system processor 216 for processing.The receive channels 410-1 to 410-M of the transceiver 214 are coupledto the system processor 216, as shown in FIG. 5 .

FIG. 5 illustrates an example scheme implemented by the radar system 102for detecting a frame-of-reference change. In the depictedconfiguration, the system processor 216 implements thehardware-abstraction module 220 and the frame-of-referencemachine-learned module 222. The system processor 216 is connected to thereceive channels 410-1 to 410-M. The system processor 216 can alsocommunicate with the computer processor 202. Although not shown, thehardware-abstraction module 220 and/or the frame-of-referencemachine-learned module 222 can be implemented by the computer processor202.

In this example, the frame-of-reference machine-learned module 222 isimplemented using a space time neural network 500, which includes amulti-stage machine-learning architecture. In the first stage, aspace-recurrent network analyzes the complex radar data 246 over aspatial domain to generate feature data. The feature data identifies oneor more features (e.g., characteristics) associated with at least oneobject's motion trajectory. The feature data is stored in a memoryelement of the circular buffer 224 for at least a portion of time. Astime progresses, other feature data is stored in other memory elementsof the circular buffer 224. The other feature data can correspond to thesame object or another object. In the second stage, a time-recurrentnetwork analyzes the feature data across multiple memory elements withinthe circular buffer 224 to detect the frame-of-reference change. Thespace time neural network 500 is further described with respect to FIGS.7-1 to 7-4 .

This multi-stage design enables the radar system 102 to conserve powerand detect the frame-of-reference change in real time (e.g., as thegesture is performed). Use of the circular buffer 224, for example,enables the radar system 102 to conserve power and memory by mitigatingthe need to regenerate the feature data or store the complex radar data246. Storing the feature data instead of the complex radar data 246 alsoreduces the computational time for detecting the frame-of-referencechange. The space time neural network 500 is also adaptable and can beexpanded to detect a frame-of-reference change in a variety of differentsituations without significantly increasing size, computationalrequirements, or latency. Additionally, the space time neural network500 can be tailored to recognize multiple types of gestures, such as aswipe gesture and a reach gesture.

In this example, the hardware-abstraction module 220 accepts the digitalbeat signals 428-1 to 428-M from the receive channels 410-1 to 410-M.The digital beat signals 428-1 to 428-M represent raw or unprocessedcomplex radar data. The hardware-abstraction module 220 performs one ormore operations to generate hardware-agnostic complex radar data 502-1to 502-M based on digital beat signals 428-1 to 428-M. Thehardware-abstraction module 220 transforms the complex radar dataprovided by the digital beat signals 428-1 to 428-M into a form that isexpected by the space time neural network 500. In some cases, thehardware-abstraction module 220 normalizes amplitudes associated withdifferent transmit power levels or transforms the complex radar datainto a frequency-domain representation.

The hardware-agnostic complex radar data 502-1 to 502-M includes bothmagnitude and phase information (e.g., in-phase and quadraturecomponents). In some implementations, the hardware-agnostic complexradar data 502-1 to 502-M includes range-Doppler maps for each receivechannel 410-1 to 410-M and for a particular active feature frame 316. Inother implementations, the hardware-agnostic complex radar data 502-1 to502-M includes complex interferometry data, which is an orthogonalrepresentation of the range-Doppler maps. As another example, thehardware-agnostic complex radar data 502-1 to 502-M includesfrequency-domain representations of the digital beat signals 428-1 to428-M for an active feature frame 316. Although not shown, otherimplementations of the radar system 102 can provide the digital beatsignals 428-1 to 428-M directly to the space time neural network 500.

The space time neural network 500 uses a trained regression orclassification model to analyze the hardware-agnostic complex radar data502-1 to 502-M and generate the frame-of-reference data 248. Althoughdescribed with respect to motion sensing, the training procedureexecuted by the space time neural network 500 and machine-learningarchitecture of the space time neural network 500 can be adapted tosupport other types of applications, including presence detection,gesture recognition, collision-avoidance, and human vital-signdetection. An example implementation of the space time neural network500 is further described with respect to FIGS. 7-1 to 7-4 .

FIG. 6 illustrates an example hardware-abstraction module 220 fordetecting a frame-of-reference change. In the depicted configuration,the hardware-abstraction module 220 includes a pre-processing stage 602and a signal-transformation stage 604. The pre-processing stage 602operates on each chirp 310-1 to 310-N within the digital beat signals428-1 to 428-M. In other words, the pre-processing stage 602 performs anoperation for each active radar frame 322. In this example, thepre-processing stage 602 includes M one-dimensional (1D) Fast-FourierTransform (FFT) modules, which respectively process the digital beatsignals 428-1 to 428-M. Other types of modules that perform similaroperations are also possible, such as a Fourier Transform module.

For simplicity, the hardware-abstraction module 220 is shown to processa digital beat signal 428-1 associated with the receive channel 410-1.The digital beat signal 428-1 includes the chirps 310-1 to 310-M, whichare time-domain signals. The chirps 310-1 to 310-M are passed to aone-dimensional FFT module 606-1 in an order in which they are receivedand processed by the transceiver 214. Assuming the radar receive signals308-1 to 308-M include 16 chirps 310-1 to 310-N (e.g., N equals 16), theone-dimensional FFT module 606-1 performs 16 FFT operations to generatepre-processed complex radar data per chirp 612-1.

The signal-transformation stage 604 operates on the sequence of chirps310-1 to 310-M within each of the digital beat signals 428-1 to 428-M.In other words, the signal-transformation stage 604 performs anoperation for each active feature frame 316. In this example, thesignal-transformation stage 604 includes M buffers and M two-dimensional(2D) FFT modules. For simplicity, the signal-transformation stage 604 isshown to include a buffer 608-1 and a two-dimensional FFT module 610-1.

The buffer 608-1 stores a first portion of the pre-processed complexradar data 612-1, which is associated with the first chirp 310-1. Theone-dimensional FFT module 606-1 continues to process subsequent chirps310-2 to 310-N, and the buffer 608-1 continues to store thecorresponding portions of the pre-processed complex radar data 612-1.This process continues until the buffer 608-1 stores a last portion ofthe pre-processed complex radar data 612-1, which is associated with thechirp 310-M.

At this point, the buffer 608-1 stores pre-processed complex radar dataassociated with a particular feature frame 614-1. The pre-processedcomplex radar data per feature frame 614-1 represents magnitudeinformation (as shown) and phase information (not shown) acrossdifferent chirps 310-1 to 310-N and across different range bins 616-1 to616-A, where A represents a positive integer.

The two-dimensional FFT 610-1 accepts the pre-processed complex radardata per feature frame 614-1 and performs a two-dimensional FFToperation to form the hardware-agnostic complex radar data 502-1, whichrepresents a range-Doppler map. The range-Doppler map includes complexradar data for the range bins 616-1 to 616-A and Doppler bins 618-1 to618-B, where B represents a positive integer. In other words, each rangebin 616-1 to 616-A and Doppler bin 618-1 to 618-B includes a complexnumber with real and imaginary parts that together represent magnitudeand phase information. The quantity of range bins 616-1 to 616-A can beon the order of tens or hundreds, such as 64 or 128 (e.g., A equals 64or 128). The quantity of Doppler bins can be on the order of tens orhundreds, such as 32, 64, or 124 (e.g., B equals 32, 64, or 124). Thehardware-agnostic complex radar data 502-1, along with thehardware-agnostic complex radar data 502-1 to 502-M (of FIG. 6-1 ), areprovided to the space time neural network 500, as shown in FIG. 7-1 .

FIG. 7-1 illustrates an example space time neural network 500 fordetecting a frame-of-reference change. In the depicted configuration,the space time neural network 500 includes two stages implemented by aspace-recurrent network 702 and a time-recurrent network 704,respectively. The space-recurrent network 702 includes a chirp-levelanalysis module 706 and a feature-level analysis module 708. In general,the space-recurrent network 702 analyzes the hardware-agnostic complexradar data 502-1 to 502-M over a spatial domain for each active featureframe 316. The resulting data is stored by the circular buffer 224. Thetime-recurrent network 704 includes a main-level analysis module 710,which analyzes data stored within the circular buffer 224 for two ormore active feature frames 316. In this manner, the time-recurrentnetwork 704 analyzes data over a temporal domain for at least a portionof an active main frame 314.

During reception, the chirp-level analysis module 706 processes thecomplex radar data across each range bin 616-1 to 616-A to generatechannel-Doppler data 712. The feature-level analysis module 708 analyzesthe channel-Doppler data 712 to generate feature data 714, whichcharacterizes one or more features for detecting the frame-of-referencechange. These features can include relative motion of one or moreobjects detected by the radar system 102. The circular buffer 224 storesthe feature data 714.

Over time, the circular buffer 224 stores feature data 714 associatedwith different active feature frames 316. Feature data 714 associatedwith two or more active feature frames 316 is referred to as compiledfeature data 716. The compiled feature data 716 is provided to oraccessed by the main-level analysis module 710. The main-level analysismodule 710 analyzes the compiled feature data 716 to theframe-of-reference data 248. As an example, the radar-application data504 includes a prediction regarding whether the frame-of-reference ismoving. As feature data 714 associated with larger quantities of activefeature frames 316 are stored by the circular buffer 224, an accuracy ofthe predictions improves. In some cases, the main-level analysis module710 continually generates or updates the radar-application data 504 assubsequent feature frames 316 associated with a main frame 314 areprocessed by the space-recurrent network 702. Alternatively, themain-level analysis module 710 delays generation of theframe-of-reference data 248 until all of the feature frames 316associated with the main frame 314 have been processed by thespace-recurrent network 702. Implementations of the chirp-level analysismodule 706, the feature-level analysis module 708, and the main-levelanalysis module 710 are further described with respect to FIGS. 7-2 to7-4 .

FIG. 7-2 illustrates an example chirp-level analysis module 706 of thespace time neural network 500. In the depicted configuration, thechirp-level analysis module 706 includes channel-Doppler processingmodules 718-1 to 718-A. Each channel-Doppler processing module 718-1 to718-A includes a neural network with one or more layers 720-1 to 720-Q,where Q is a positive integer. The value of Q can vary depending on theimplementation. As an example, Q can equal 2, 4, or 10. The layers 720-1to 720-Q can be fully connected or partially connected. Nodes within thelayers 720-1 to 720-Q execute a non-linear rectifier activationfunction, for instance. The channel-Doppler processing modules 718-1 to718-A can also perform additions and multiplications.

The channel-Doppler processing modules 718-1 to 718-A accept respectiveportions of the hardware-agnostic complex radar data 502-1 to 502-Maccording to the range bins 616-1 to 616-A. In particular, thechannel-Doppler processing module 718-1 accepts the complex radar dataassociated with the first range bin 616-1 across all of the receivechannels 410-1 to 410-M and across all of the Doppler bins 618-1 to618-B. Each complex number is provided as an input to individual nodesof the layer 720-1. The layers 720-1 to 720-Q analyze the data using thenon-linear rectifier activation function to generate channel-Dopplerdata 712-1. Similar operations are also performed by the channel-Dopplerprocessing modules 718-2 to 718-A. The combined channel-Doppler data712-1 to 712-A represents a vector. For example, assuming there arethree receive channels 410-1 to 410-M (e.g., M equals 3), 32 Dopplerbins 618-1 to 618-B (e.g., B equals 32) and 16 range bins 616-1 to 616-A(e.g., A equals 16), the channel-Doppler data 712-1 to 712-A forms a1×16 vector of values, which represents a relationship across thereceive channels in the Doppler domain to enable the feature-levelanalysis module 708 to identify one or more features associated with theframe-of-reference change.

FIG. 7-3 illustrates an example feature-level analysis module 708 of thespace time neural network 500. In the depicted configuration, thefeature-level analysis module 708 is implemented using one or morerecurrent layers 722-1 to 722-V, where V represents a positive integer.Within the recurrent layers 722-1 to 722-V, connections between thenodes form a cycle, which retains information from a previous activefeature frame 316 for a subsequent active feature frame 316. Using therecurrent layers 722-1 to 722-V, the feature-level analysis module 708can implement a long-short-term memory (LSTM) network, for instance.

As described above, the feature-level analysis module 708 processes thechannel-Doppler data 712-1 to 712-A across the range bins 616-1 to 616-Ato generate the feature data 714. Although not shown, someimplementations of the space-recurrent network 702 can includeadditional fully-connected layers 720 connected to outputs of therecurrent layer 722-V. Similar to the layers 720 of the chirp-levelanalysis module 706, these layers 720 can also perform non-lineartransformations.

Over time, feature data 714-1 to 714-J associated with active featureframes 316-1 to 316-J are sequentially stored by the circular buffer 224in different memory elements. The feature data 714-1 to 714-J representsthe compiled feature data 716, which is processed by the main-levelanalysis module 710, as further described with respect to FIG. 7-4 .

FIG. 7-4 illustrates an example main-level analysis module 710 of thespace time neural network 500. In the depicted configuration, themain-level analysis module 710 is implemented using one or morerecurrent layers 724-1 to 724-T, where T represents a positive integerthat may or may not be equal to V. Using the recurrent layers 724-1 to724-T, the main-level analysis module 710 can implement along-short-term memory (LSTM) network, for instance.

As described above, the main-level analysis module 710 processes two ormore feature data 714-1 to 714-J stored within the circular buffer 224.For example, the main-level analysis module 710 forms a predictionregarding whether or not the frame-of-reference is moving based on twoor more of the feature data 714-1 to 714-J. In some cases, themain-level analysis module 710 can wait to form the prediction until aspecified quantity of feature data 714-1 to 714-J is available, such as15. If the active main frame 314 includes more than 15 active featureframes 316 (e.g., J is greater than 15), the main-level analysis module710 can continue to update its prediction based on the last 15 activefeature frames 316. In general, an accuracy of the prediction increasesover time or when larger quantities of feature data 714-1 to 714-J isanalyzed.

Example Method

FIG. 8 depicts an example method 800 for performing operations of asmart-device-based radar system capable of detecting aframe-of-reference change. Method 800 is shown as sets of operations (oracts) performed but not necessarily limited to the order or combinationsin which the operations are shown herein. Further, any of one or more ofthe operations may be repeated, combined, reorganized, or linked toprovide a wide array of additional and/or alternate methods. In portionsof the following discussion, reference may be made to the environment100-1 to 100-8 of FIG. 1 , and entities detailed in FIG. 2, 4 , or 5,reference to which is made for example only. The techniques are notlimited to performance by one entity or multiple entities operating onone device.

At 802, a first radar transmit signal is transmitted. For example, theradar system 102 uses at least one transmit antenna element 420 totransmit the radar transmit signal 306, as shown in FIG. 4 . In someimplementations, the radar transmit signal 306 includes multiple chirps310-1 to 310-N, whose frequencies are modulated, as shown in FIG. 3-1 .

At 804, a first radar receive signal is received. The first radarreceive signal comprises a version of the first radar transmit signalthat is reflected by at least one user. For example, the radar system102 uses at least one receive antenna element 422 to receive a versionof the radar receive signal 308 that is reflected by the user 302, asshown in FIG. 4 .

At 806, complex radar data is generated based on the first radar receivesignal. For example, a receive channel 410 of the radar system 102generates a digital beat signal 428 based on the radar receive signal308. The digital beat signal represents complex radar data 246 andincludes both magnitude and phase information.

At 808, the complex radar data is analyzed using machine learning todetect a change in the radar system's frame of reference. For example,the frame-of-reference machine-learned module 222 analyzes the digitalbeat signals 428-1 to 428-M or the hardware-agnostic complex radar data502-1 to 502-M to generate the frame-of-reference data 248, whichprovides information regarding the radar system 102's frame ofreference. This information can indicate whether the frame of referenceis stationary, whether the frame of reference is moving, and/orcharacteristics regarding changes to the frame of reference (e.g.,distance, direction, velocity). Although described for motion sensing,similar operations can also be performed for other applications,including presence detection, gesture recognition, collision avoidance,vital sign detection, and so forth.

Example Computing System

FIG. 9 illustrates various components of an example computing system 900that can be implemented as any type of client, server, and/or computingdevice as described with reference to the previous FIG. 2 to detect aframe-of-reference change.

The computing system 900 includes communication devices 902 that enablewired and/or wireless communication of device data 904 (e.g., receiveddata, data that is being received, data scheduled for broadcast, or datapackets of the data). Although not shown, the communication devices 902or the computing system 900 can include one or more radar systems 102.The device data 904 or other device content can include configurationsettings of the device, media content stored on the device, and/orinformation associated with a user 302 of the device. Media contentstored on the computing system 900 can include any type of audio, video,and/or image data. The computing system 900 includes one or more datainputs 906 via which any type of data, media content, and/or inputs canbe received, such as human utterances, the radar-based application 206,user-selectable inputs (explicit or implicit), messages, music,television media content, recorded video content, and any other type ofaudio, video, and/or image data received from any content and/or datasource.

The computing system 900 also includes communication interfaces 908,which can be implemented as any one or more of a serial and/or parallelinterface, a wireless interface, any type of network interface, a modem,and as any other type of communication interface. The communicationinterfaces 908 provide a connection and/or communication links betweenthe computing system 900 and a communication network by which otherelectronic, computing, and communication devices communicate data withthe computing system 900.

The computing system 900 includes one or more processors 910 (e.g., anyof microprocessors, controllers, and the like), which process variouscomputer-executable instructions to control the operation of thecomputing system 900 and to enable techniques for, or in which can beembodied, gesture recognition in the presence of saturation.Alternatively or in addition, the computing system 900 can beimplemented with any one or combination of hardware, firmware, or fixedlogic circuitry that is implemented in connection with processing andcontrol circuits which are generally identified at 912. Although notshown, the computing system 900 can include a system bus or datatransfer system that couples the various components within the device. Asystem bus can include any one or combination of different busstructures, such as a memory bus or memory controller, a peripheral bus,a universal serial bus, and/or a processor or local bus that utilizesany of a variety of bus architectures.

The computing system 900 also includes a computer-readable media 914,such as one or more memory devices that enable persistent and/ornon-transitory data storage (i.e., in contrast to mere signaltransmission), examples of which include random access memory (RAM),non-volatile memory (e.g., any one or more of a read-only memory (ROM),flash memory, EPROM, EEPROM, etc.), and a disk storage device. The diskstorage device may be implemented as any type of magnetic or opticalstorage device, such as a hard disk drive, a recordable and/orrewriteable compact disc (CD), any type of a digital versatile disc(DVD), and the like. The computing system 900 can also include a massstorage media device (storage media) 916.

The computer-readable media 914 provides data storage mechanisms tostore the device data 904, as well as various device applications 918and any other types of information and/or data related to operationalaspects of the computing system 900. For example, an operating system920 can be maintained as a computer application with thecomputer-readable media 914 and executed on the processors 910. Thedevice applications 918 may include a device manager, such as any formof a control application, software application, signal-processing andcontrol module, code that is native to a particular device, a hardwareabstraction layer for a particular device, and so on.

The device applications 918 also include any system components, engines,or managers to detect a frame-of-reference change. In this example, thedevice applications 918 includes the radar-based application 206 and theframe-of-reference machine-learned module 222 of FIG. 2 .

Conclusion

Although techniques using, and apparatuses including, asmart-device-based radar system detecting a frame-of-reference changehave been described in language specific to features and/or methods, itis to be understood that the subject of the appended claims is notnecessarily limited to the specific features or methods described.Rather, the specific features and methods are disclosed as exampleimplementations of a smart-device-based radar system detecting aframe-of-reference change.

Some examples are described below.

Example 1: A method performed by a radar system, the method comprising:

transmitting a first radar transmit signal;receiving a first radar receive signal, the first radar receive signalcomprising a version of the first radar transmit signal that isreflected by at least one object;generating complex radar data based on the first radar receive signal;andanalyzing the complex radar data using machine learning to detect achange in the radar system's frame of reference.

Example 2: The method of example 1, wherein the at least one objectcomprises at least one user,

the method further comprising:

-   -   determining that the radar system is moving based on the        detected change in the radar system's frame of reference; and    -   responsive to determining that the radar system is moving,        determining that the at least one user did not perform a        gesture.

Example 3: The method of example 1 or example 2, further comprising:

transmitting a second radar transmit signal;receiving a second radar receive signal, the second radar receive signalcomprising a version of the second radar transmit signal that isreflected by at least one other object;generating other complex radar data based on the second radar receivesignal; andanalyzing the other complex radar data using the machine learning todetermine that the radar system is stationary.

Example 4: The method of example 3, wherein the at least one otherobject comprises at least one other user,

the method further comprising:

-   -   responsive to determining that the radar system is stationary,        recognizing, based on the other complex radar data, a gesture        performed by the at least one other user.

Example 5: The method of any preceding example, wherein:

the at least one object comprises a first object and a second object;andthe analyzing the complex radar data comprises:

-   -   determining relative motion of the first object based on the        complex radar data;    -   determining relative motion of the second object based on the        complex radar data; and    -   detecting the change in the radar system's frame of reference by        using the machine learning to compare the relative motion of the        first object with the relative motion of the second object.

Example 6: The method of example 5, wherein:

the first object is stationary and the second object is stationary;the first object is stationary and the second object is moving; orthe first object is moving and the second object is moving.

Example 7: The method of example 5, wherein the determining the relativemotion of the first object, the determining the relative motion of thesecond object, and the detecting the change in the radar system's frameof reference comprises:

-   -   analyzing, using a space-recurrent network, the complex radar        data over a spatial domain to generate feature data; and    -   analyzing, using a time-recurrent network, the feature data over        a temporal domain to recognize the gesture.

Example 8: The method of example 7, further comprising:

storing the feature data within a circular buffer; andaccessing, by the time-recurrent network, the feature data stored withinthe circular buffer.

Example 9: The method of example 7 or 8, wherein the analyzing thecomplex radar data over the spatial domain comprises:

-   -   separately processing portions of the complex radar data        associated with different range bins using a non-linear        activation function to generate channel-Doppler data for each        range bin; and    -   analyzing the channel-Doppler data across the different range        bins to generate the feature data.

Example 10: The method of any of examples 7 to 9, wherein the analyzingthe feature data over the temporal domain comprises forming a predictionregarding a likelihood of the radar system's frame of reference movingand the radar system's frame of reference being stationary.

Example 11: The method of any preceding example, wherein the analyzingthe complex radar data comprises analyzing magnitude and/or phaseinformation of the complex radar data using the machine learning.

Example 12: The method of any preceding example, wherein the complexradar data comprises in-phase components and quadrature components.

Example 13: The method of any preceding example, wherein the complexradar data comprises at least one of the following:

a complex range-Doppler map;complex interferometry data;multiple digital beat signals associated with the radar receive signal;orfrequency-domain representations of the multiple digital beat signals.

Example 14: The method of any preceding example, wherein the first radartransmit signal comprises at least one chirp.

Example 15: The method of any preceding example, wherein the machinelearning uses at least one artificial neural network, in particular atleast one artificial neural network with a deep neural network, inparticular with a recurrent deep neural network.

Example 16: The method of any preceding example wherein the machinelearning comprises supervised learning, in particular using real datafor machine-learning training purposes.

Example 17: The method of any preceding example, wherein the machinelearning comprises offline training and/or real-time training.

Example 18: The method of example 1, wherein:

the radar system is part of a smart device; andthe smart device does not include an inertial sensor or does not use theinertial sensor to detect the change in the radar system's frame ofreference.

Example 19: An apparatus comprising:

a radar system comprising:

-   -   an antenna array;    -   a transceiver; and    -   a processor and computer-readable storage media configured to        perform any of the methods of examples 1 to 17.

Example 20: The apparatus of example 19, wherein the apparatus comprisesa smart device, the smart device comprising one of the following:

a smartphone;a smart watch;a smart speaker;a smart thermostat;a security camera;a vehicle; ora household appliance.

Example 21: The apparatus of example 19, wherein:

the apparatus comprises a smart device; andthe smart device does not include an inertial sensor or does not use theinertial sensor to detect the change in the radar system's frame ofreference.

Example 22: A computer-readable storage media comprisingcomputer-executable instructions that, responsive to execution by aprocessor, implement:

a frame-of-reference machine-learned module configured to:

-   -   accept complex radar data associated with a radar receive signal        that is reflected by at least one object;    -   analyze the complex radar data using machine learning to        generate frame-of-reference data; and    -   determine, based on the frame-of-reference data, whether or not        an antenna array that received the radar receive signal is        stationary or moving.

Example 23: The computer-readable storage media of example 22, whereinthe frame-of-reference machine-learned module is further configured toanalyze both magnitude and phase information of the complex radar datato generate the frame-of-reference data.

Example 24: The computer-readable storage media of example 22 or example23, wherein the frame-of-reference machine-learned module is furtherconfigured to:

-   -   analyze, using the machine learning, the complex radar data over        a spatial domain to generate feature data; and    -   analyze, using the machine learning, the feature data over a        temporal domain to generate radar-application data.

Example 25: The computer-readable storage media of any of examples 22 to24, wherein the computer-executable instructions, responsive toexecution by the processor, implement a hardware-abstraction moduleconfigured to:

-   -   generate hardware-agnostic complex radar data based on the        complex radar data; and    -   provide, to the frame-of-reference machine-learned module, the        hardware-agnostic complex radar data as the complex radar data.

Example 26: The computer-readable storage media of example 22, wherein:

-   -   the computer-readable storage media and the processor are part        of a smart device; and    -   the smart device does not include an inertial sensor or does not        use the inertial sensor to detect the change in the radar        system's frame of reference.

1. A method comprising: generating complex radar data based on a first radar receive signal, the first radar receive signal comprising a version of a first radar transmit signal that is reflected by at least one object; and detecting, based on a machine-learned model, a change in a radar system's frame of reference, the machine-learned model configured to compare a relative motion of a first object of the at least one object and a relative motion of a second object of the at least one object, the relative motion of the first object and the relative motion of the second object determined based on the complex radar data.
 2. The method of claim 1, wherein the at least one object comprises at least one user and further comprising determining that the at least one user did not perform a gesture based on a determination that the radar system is moving, the determination based on the detected change in the radar system's frame of reference.
 3. The method of claim 1, further comprising: generating additional complex radar data based on a second radar receive signal, the second radar receive signal comprising a version of a second radar transmit signal that is reflected by at least one other object; and detecting, based on the machine-learned model, no change in the radar system's frame of reference, the no change useful to determine that the radar system is stationary.
 4. The method of claim 1, further comprising: responsive to detecting the change in the radar system's frame of reference and based on at least one of the relative motion of the first object or the relative motion of the second object, compensating for a relative motion of the radar system; and recognizing, based on the compensated relative motion of the radar system, a gesture of a user.
 5. The method of claim 1, wherein the relative motion of the first object, the relative motion of the second object, and the detecting the change in the radar system's frame of reference are further based on: analyzing, using a space-recurrent network, the complex radar data over a spatial domain to generate feature data; and analyzing, using a time-recurrent network, the feature data over a temporal domain to recognize a gesture.
 6. The method of claim 5, further comprising: storing the feature data within a circular buffer; and accessing, by the time-recurrent network, the feature data stored within the circular buffer.
 7. The method of claim 5, wherein the analyzing the complex radar data over the spatial domain comprises: separately processing portions of the complex radar data associated with different range bins using a non-linear activation function to generate channel-Doppler data for each range bin; and analyzing the channel-Doppler data across the different range bins to generate the feature data.
 8. The method of claim 5, wherein the analyzing the feature data over the temporal domain comprises forming a prediction regarding a likelihood of the radar system's frame of reference moving and the radar system's frame of reference being stationary.
 9. The method of claim 1, wherein the complex radar data comprises at least one of the following: a complex range-Doppler map; complex interferometry data; multiple digital beat signals associated with the radar receive signal; or frequency-domain representations of the multiple digital beat signals.
 10. The method of claim 1, wherein the radar system comprises a plurality of antenna arrays and a plurality of transceivers, the plurality of antenna arrays and the plurality of transceivers distributed amongst a first device and a second device, and wherein the first radar transmit signal is transmitted by the first device and the first radar receive signal is received by the second device, the method further comprising: transmitting, from a communication interface of the first device to a communication interface of the second device, data associated with the first radar transmit signal, and wherein generating complex radar data is further based on the data associated with the first radar transmit signal.
 11. The method of claim 1, wherein the first radar transmit signal comprises at least one chirp.
 12. The method of claim 1, wherein the radar system is integrated within a smart device, and wherein generating complex radar data and detecting a change in a radar system's frame of reference is performed on a remote device, the method further comprising: receiving, prior to generating complex radar data, the first radar receive signal via a communication interface of the smart device; and transmitting, based on detecting a change in a radar system's frame of reference, instructions to a radar-based application integrated on the smart device.
 13. The method of claim 1, wherein the machine-learned model comprises at least one artificial neural network with a deep neural network.
 14. The method of claim 13, wherein the deep neural network comprises a recurrent deep neural network.
 15. The method of claim 1, wherein the machine-learned model is trained using supervised learning.
 16. The method of claim 1, wherein: the radar system is integral to a smart device, and the smart device does not include an inertial sensor or does not use the inertial sensor to detect the change in the radar system's frame of reference.
 17. The method of claim 1, wherein analyzing the complex radar data comprises analyzing at least one of magnitude information or phase information of the complex radar data using the machine-learned model.
 18. The method of claim 1, wherein the complex radar data comprises in-phase components and quadrature components.
 19. An apparatus comprising: a radar system configured to: generate complex radar data based on a first radar receive signal, the first radar receive signal comprising a version of a first radar transmit signal that is reflected by at least one object; and detect, based on a machine-learned model, a change in the radar system's frame of reference, the machine-learned model configured to compare a relative motion of a first object of the at least one object and a relative motion of a second object of the at least one object, the relative motion of the first object and the relative motion of the second object determined based on the complex radar data.
 20. The apparatus of claim 19, wherein: the at least one object comprises at least one user; and the radar system is configured to: determine that the radar system is moving based on the detected change in the radar system's frame of reference; and responsive to determining that the radar system is moving, determine that the at least one user did not perform a gesture.
 21. The apparatus of claim 19, wherein the radar system is further configured to: generate additional complex radar data based on a second radar receive signal, the second radar receive signal comprising a version of a second radar transmit signal that is reflected by at least one other object; and detect, based on the machine-learned model, no change in the radar system's frame of reference, the no change useful to determine that the radar system is stationary.
 22. The apparatus of claim 19, wherein the radar system is further configured to: responsive to the detection of the change in the radar system's frame of reference and based on at least one of the relative motion of the first object or the relative motion of the second object, compensate for a relative motion of the radar system; and recognize, based on the compensation for the relative motion of the radar system, a gesture of a user.
 23. The apparatus of claim 19, wherein the relative motion of the first object, the relative motion of the second object, and the detection of the change in the radar system's frame of reference are based on: analysis of the complex radar data over a spatial domain to generate feature data; and analysis of the feature data over a temporal domain to recognize a gesture.
 24. The apparatus of claim 23, wherein: analysis of the complex radar data over a spatial domain is based on a space-recurrent network, and analysis of the feature data over a temporal domain is based on a time-recurrent network.
 25. The apparatus of claim 23, wherein the radar system is further configured to: store the feature data within a circular buffer; and access, by the time-recurrent network, the feature data stored within the circular buffer.
 26. The apparatus of claim 23, wherein the analysis the complex radar data over the spatial domain is further based on: separately process portions of the complex radar data associated with different range bins using a non-linear activation function to generate channel-Doppler data for each range bin; and analysis of the channel-Doppler data across the different range bins to generate the feature data.
 27. The apparatus of claim 23, wherein the analysis of the feature data over the temporal domain is further based on a prediction regarding a likelihood of the radar system's frame of reference moving and the radar system's frame of reference being stationary.
 28. The apparatus of claim 19, wherein the complex radar data comprises at least one of the following: a complex range-Doppler map; complex interferometry data; multiple digital beat signals associated with the radar receive signal; or frequency-domain representations of the multiple digital beat signals.
 29. The apparatus of claim 19, wherein the first radar transmit signal comprises at least one chirp.
 30. The apparatus of claim 19, wherein the machine-learned model comprises at least one artificial neural network with a deep neural network.
 31. The apparatus of claim 30, wherein the deep neural network comprises a recurrent deep neural network.
 32. The apparatus of claim 19, wherein the machine-learned model is trained using supervised learning.
 33. The apparatus of claim 19, wherein analyzing the complex radar data comprises analyzing at least one of magnitude information or phase information of the complex radar data using the machine-learned model.
 34. The apparatus of claim 19, wherein the complex radar data comprises in-phase components and quadrature components.
 35. The apparatus of claim 19, wherein the apparatus comprises a smart device, the smart device comprising one of the following: a smartphone; a smart watch; a smart speaker; a smart thermostat; a security camera; a vehicle; or a household appliance.
 36. The apparatus of claim 19, wherein: the apparatus comprises a smart device; and the smart device does not include an inertial sensor or does not use the inertial sensor to detect the change in the radar system's frame of reference.
 37. The apparatus of claim 19, wherein: the apparatus comprises a smart device; and the smart device does not include a camera sensor or does not use the camera sensor to detect the change in the radar system's frame of reference.
 38. A computer-readable storage media comprising computer-executable instructions that, responsive to execution by a processor, implement: a frame-of-reference machine-learned module configured to: obtain complex radar data associated with a radar receive signal that is reflected by at least one object; and detect, based on a machine-learned model, a change in a radar system's frame of reference, the machine-learned model configured to compare a relative motion of a first object of the at least one object and a relative motion of a second object of the at least one object, the relative motion of the first object and the relative motion of the second object determined based on the complex radar.
 39. The computer-readable storage media of claim 38, wherein the frame-of-reference machine-learned module is further configured to analyze at least one of magnitude information or phase information of the complex radar data to generate the frame-of-reference data.
 40. The computer-readable storage media of claim 38, wherein the frame-of-reference machine-learned module is further configured to: analyze the complex radar data over a spatial domain to generate feature data; and analyze the feature data over a temporal domain to generate radar-application data.
 41. The computer-readable storage media of claim 40, wherein: analysis of the complex radar data over the spatial domain is based on a space-recurrent network, and analysis of the feature data over the temporal domain is based on a time-recurrent network.
 42. The computer-readable storage media of claim 40, wherein the analyzing the complex radar data over the spatial domain comprises: separately processing portions of the complex radar data associated with different range bins using a non-linear activation function to generate channel-Doppler data for each range bin; and analyzing the channel-Doppler data across the different range bins to generate the feature data.
 43. The computer-readable storage media of claim 40, wherein the analyzing the feature data over the temporal domain comprises forming a prediction regarding a likelihood of the radar system moving and the radar system being stationary.
 44. The computer-readable storage media of claim 38, wherein the computer-executable instructions, responsive to execution by the processor, implement a hardware-abstraction module configured to: generate hardware-agnostic complex radar data based on the complex radar data; and provide, to the frame-of-reference machine-learned module, the hardware-agnostic complex radar data as the complex radar data.
 45. The computer-readable storage media of claim 38, wherein: the computer-readable storage media and the processor are part of a smart device; and the smart device does not include an inertial sensor or does not use the inertial sensor to detect the change in the radar system's frame of reference.
 46. The computer-readable storage media of claim 38, wherein the complex radar data comprises at least one of the following: a complex range-Doppler map; complex interferometry data; multiple digital beat signals associated with the radar receive signal; or frequency-domain representations of the multiple digital beat signals.
 47. The computer-readable storage media of claim 38, wherein the first radar transmit signal comprises at least one chirp.
 48. The computer-readable storage media of claim 38, wherein the machine-learned model comprises at least one artificial neural network with a deep neural network.
 49. The computer-readable storage media of claim 48, wherein the deep neural network comprises a recurrent deep neural network.
 50. The computer-readable storage media of claim 38, wherein the machine-learned model is trained using supervised learning.
 51. The computer-readable storage media of claim 38, wherein the complex radar data comprises in-phase components and quadrature components. 